Overview

Brought to you by YData

Dataset statistics

Number of variables19
Number of observations108172
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory69.6 MiB
Average record size in memory675.0 B

Variable types

Text1
Numeric8
Categorical10

Alerts

AverageLeadTime is highly overall correlated with BookingsCheckedIn and 4 other fieldsHigh correlation
BookingsCheckedIn is highly overall correlated with AverageLeadTime and 4 other fieldsHigh correlation
DistributionChannel is highly overall correlated with MarketSegmentHigh correlation
LodgingRevenue is highly overall correlated with AverageLeadTime and 4 other fieldsHigh correlation
MarketSegment is highly overall correlated with DistributionChannelHigh correlation
OtherRevenue is highly overall correlated with AverageLeadTime and 4 other fieldsHigh correlation
PersonsNights is highly overall correlated with AverageLeadTime and 4 other fieldsHigh correlation
RoomNights is highly overall correlated with AverageLeadTime and 4 other fieldsHigh correlation
DistributionChannel is highly imbalanced (57.3%) Imbalance
SRAccessibleRoom is highly imbalanced (99.7%) Imbalance
SRCrib is highly imbalanced (87.9%) Imbalance
SRNoAlcoholInMiniBar is highly imbalanced (99.7%) Imbalance
SRQuietRoom is highly imbalanced (56.3%) Imbalance
Floor_asked is highly imbalanced (87.9%) Imbalance
Bath asked is highly imbalanced (96.9%) Imbalance
Distance_elevator_asked is highly imbalanced (97.5%) Imbalance
BookingsCheckedIn is highly skewed (γ1 = 26.76790705) Skewed
AverageLeadTime has 34750 (32.1%) zeros Zeros
LodgingRevenue has 31841 (29.4%) zeros Zeros
OtherRevenue has 31624 (29.2%) zeros Zeros
BookingsCheckedIn has 31281 (28.9%) zeros Zeros
PersonsNights has 31285 (28.9%) zeros Zeros
RoomNights has 31281 (28.9%) zeros Zeros

Reproduction

Analysis started2025-03-03 15:07:31.151917
Analysis finished2025-03-03 15:07:46.584802
Duration15.43 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

Distinct199
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size7.0 MiB
2025-03-03T15:07:47.192003image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters324516
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique31 ?
Unique (%)< 0.1%

Sample

1st rowPRT
2nd rowPRT
3rd rowDEU
4th rowFRA
5th rowFRA
ValueCountFrequency (%)
fra 16124
14.9%
deu 13801
12.8%
prt 13131
12.1%
gbr 11206
10.4%
esp 5934
 
5.5%
usa 5323
 
4.9%
ita 4212
 
3.9%
bel 4063
 
3.8%
bra 3978
 
3.7%
nld 3760
 
3.5%
Other values (189) 26640
24.6%
2025-03-03T15:07:47.742220image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
R 53085
16.4%
A 36040
11.1%
E 29112
9.0%
U 24794
 
7.6%
P 20760
 
6.4%
T 19999
 
6.2%
B 19826
 
6.1%
D 18895
 
5.8%
F 17134
 
5.3%
S 16558
 
5.1%
Other values (16) 68313
21.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 324516
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
R 53085
16.4%
A 36040
11.1%
E 29112
9.0%
U 24794
 
7.6%
P 20760
 
6.4%
T 19999
 
6.2%
B 19826
 
6.1%
D 18895
 
5.8%
F 17134
 
5.3%
S 16558
 
5.1%
Other values (16) 68313
21.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 324516
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
R 53085
16.4%
A 36040
11.1%
E 29112
9.0%
U 24794
 
7.6%
P 20760
 
6.4%
T 19999
 
6.2%
B 19826
 
6.1%
D 18895
 
5.8%
F 17134
 
5.3%
S 16558
 
5.1%
Other values (16) 68313
21.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 324516
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
R 53085
16.4%
A 36040
11.1%
E 29112
9.0%
U 24794
 
7.6%
P 20760
 
6.4%
T 19999
 
6.2%
B 19826
 
6.1%
D 18895
 
5.8%
F 17134
 
5.3%
S 16558
 
5.1%
Other values (16) 68313
21.1%

Age
Real number (ℝ)

Distinct334
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.478497
Minimum0
Maximum123
Zeros17
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2025-03-03T15:07:47.931494image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile15
Q134
median46
Q357
95-th percentile73
Maximum123
Range123
Interquartile range (IQR)23

Descriptive statistics

Standard deviation16.911532
Coefficient of variation (CV)0.37185777
Kurtosis-0.3185102
Mean45.478497
Median Absolute Deviation (MAD)12
Skewness-0.16035518
Sum4919500
Variance285.99993
MonotonicityNot monotonic
2025-03-03T15:07:48.130336image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
51 2570
 
2.4%
52 2546
 
2.4%
55 2521
 
2.3%
50 2506
 
2.3%
54 2463
 
2.3%
48 2440
 
2.3%
49 2429
 
2.2%
53 2415
 
2.2%
56 2336
 
2.2%
47 2280
 
2.1%
Other values (324) 83666
77.3%
ValueCountFrequency (%)
0 17
 
< 0.1%
1 137
 
0.1%
2 228
0.2%
3 192
0.2%
4 207
0.2%
5 215
0.2%
6 266
0.2%
7 318
0.3%
8 405
0.4%
9 409
0.4%
ValueCountFrequency (%)
123 1
 
< 0.1%
115 2
< 0.1%
114 3
< 0.1%
111 2
< 0.1%
110 1
 
< 0.1%
97 1
 
< 0.1%
95 1
 
< 0.1%
93 4
< 0.1%
92 2
< 0.1%
91 3
< 0.1%

DaysSinceCreation
Real number (ℝ)

Distinct1349
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean597.50391
Minimum36
Maximum1385
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2025-03-03T15:07:48.503284image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum36
5-th percentile81
Q1290
median525
Q3892
95-th percentile1263
Maximum1385
Range1349
Interquartile range (IQR)602

Descriptive statistics

Standard deviation374.42384
Coefficient of variation (CV)0.62664668
Kurtosis-0.98037904
Mean597.50391
Median Absolute Deviation (MAD)295
Skewness0.38982481
Sum64633193
Variance140193.21
MonotonicityNot monotonic
2025-03-03T15:07:48.661435image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
502 244
 
0.2%
522 231
 
0.2%
312 229
 
0.2%
391 219
 
0.2%
571 219
 
0.2%
137 214
 
0.2%
108 206
 
0.2%
368 203
 
0.2%
485 199
 
0.2%
507 199
 
0.2%
Other values (1339) 106009
98.0%
ValueCountFrequency (%)
36 6
 
< 0.1%
37 105
0.1%
38 67
 
0.1%
39 172
0.2%
40 109
0.1%
41 139
0.1%
42 140
0.1%
43 99
0.1%
44 106
0.1%
45 95
0.1%
ValueCountFrequency (%)
1385 68
0.1%
1384 87
0.1%
1383 102
0.1%
1382 16
 
< 0.1%
1381 92
0.1%
1380 21
 
< 0.1%
1379 10
 
< 0.1%
1378 15
 
< 0.1%
1377 5
 
< 0.1%
1376 20
 
< 0.1%

AverageLeadTime
Real number (ℝ)

High correlation  Zeros 

Distinct424
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean60.349943
Minimum-1
Maximum588
Zeros34750
Zeros (%)32.1%
Negative13
Negative (%)< 0.1%
Memory size1.7 MiB
2025-03-03T15:07:48.785178image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile0
Q10
median22
Q394
95-th percentile233
Maximum588
Range589
Interquartile range (IQR)94

Descriptive statistics

Standard deviation83.724841
Coefficient of variation (CV)1.3873226
Kurtosis4.3525687
Mean60.349943
Median Absolute Deviation (MAD)22
Skewness1.9066941
Sum6528174
Variance7009.849
MonotonicityNot monotonic
2025-03-03T15:07:48.901622image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 34750
32.1%
1 2111
 
2.0%
2 1265
 
1.2%
6 1235
 
1.1%
4 1210
 
1.1%
3 1180
 
1.1%
5 1171
 
1.1%
7 1152
 
1.1%
8 1068
 
1.0%
9 818
 
0.8%
Other values (414) 62212
57.5%
ValueCountFrequency (%)
-1 13
 
< 0.1%
0 34750
32.1%
1 2111
 
2.0%
2 1265
 
1.2%
3 1180
 
1.1%
4 1210
 
1.1%
5 1171
 
1.1%
6 1235
 
1.1%
7 1152
 
1.1%
8 1068
 
1.0%
ValueCountFrequency (%)
588 19
< 0.1%
574 10
< 0.1%
549 19
< 0.1%
546 10
< 0.1%
543 2
 
< 0.1%
542 5
 
< 0.1%
541 5
 
< 0.1%
535 22
< 0.1%
534 1
 
< 0.1%
533 2
 
< 0.1%

LodgingRevenue
Real number (ℝ)

High correlation  Zeros 

Distinct12689
Distinct (%)11.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean289.67318
Minimum0
Maximum21781
Zeros31841
Zeros (%)29.4%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2025-03-03T15:07:49.026472image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median216
Q3400.5
95-th percentile892.5
Maximum21781
Range21781
Interquartile range (IQR)400.5

Descriptive statistics

Standard deviation382.62082
Coefficient of variation (CV)1.3208707
Kurtosis148.2624
Mean289.67318
Median Absolute Deviation (MAD)216
Skewness6.15568
Sum31334527
Variance146398.69
MonotonicityNot monotonic
2025-03-03T15:07:49.134999image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 31841
29.4%
176 745
 
0.7%
234 579
 
0.5%
126 565
 
0.5%
264 524
 
0.5%
249 512
 
0.5%
168 449
 
0.4%
89 403
 
0.4%
210 318
 
0.3%
178 298
 
0.3%
Other values (12679) 71938
66.5%
ValueCountFrequency (%)
0 31841
29.4%
18 2
 
< 0.1%
22 1
 
< 0.1%
24 5
 
< 0.1%
25 1
 
< 0.1%
28 1
 
< 0.1%
34 4
 
< 0.1%
35 1
 
< 0.1%
36 2
 
< 0.1%
37 1
 
< 0.1%
ValueCountFrequency (%)
21781 1
< 0.1%
14044.8 1
< 0.1%
9682.4 1
< 0.1%
9665.66 1
< 0.1%
9180 1
< 0.1%
9010 1
< 0.1%
8493.65 1
< 0.1%
7902 1
< 0.1%
7458 1
< 0.1%
7256 1
< 0.1%

OtherRevenue
Real number (ℝ)

High correlation  Zeros 

Distinct5338
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.796411
Minimum0
Maximum8859.25
Zeros31624
Zeros (%)29.2%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2025-03-03T15:07:49.250636image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median32
Q385
95-th percentile238.5
Maximum8859.25
Range8859.25
Interquartile range (IQR)85

Descriptive statistics

Standard deviation125.05846
Coefficient of variation (CV)1.9006882
Kurtosis569.3763
Mean65.796411
Median Absolute Deviation (MAD)32
Skewness14.817419
Sum7117329.4
Variance15639.619
MonotonicityNot monotonic
2025-03-03T15:07:49.361371image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 31624
29.2%
42 2925
 
2.7%
14 2736
 
2.5%
28 2195
 
2.0%
56 1680
 
1.6%
7 1648
 
1.5%
21 1276
 
1.2%
2 1085
 
1.0%
16 1062
 
1.0%
8 1020
 
0.9%
Other values (5328) 60921
56.3%
ValueCountFrequency (%)
0 31624
29.2%
1 369
 
0.3%
1.9 1
 
< 0.1%
2 1085
 
1.0%
2.1 3
 
< 0.1%
2.2 2
 
< 0.1%
2.4 1
 
< 0.1%
2.5 3
 
< 0.1%
3 184
 
0.2%
3.24 1
 
< 0.1%
ValueCountFrequency (%)
8859.25 1
 
< 0.1%
5268.5 1
 
< 0.1%
5261 1
 
< 0.1%
5237 7
< 0.1%
5105.5 1
 
< 0.1%
4296 1
 
< 0.1%
3692.4 1
 
< 0.1%
3580.5 1
 
< 0.1%
3190.4 1
 
< 0.1%
3050.85 1
 
< 0.1%

BookingsCheckedIn
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct33
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.74663499
Minimum0
Maximum76
Zeros31281
Zeros (%)28.9%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2025-03-03T15:07:49.471985image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile1
Maximum76
Range76
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.73547998
Coefficient of variation (CV)0.98505962
Kurtosis1956.6324
Mean0.74663499
Median Absolute Deviation (MAD)0
Skewness26.767907
Sum80765
Variance0.5409308
MonotonicityNot monotonic
2025-03-03T15:07:49.580019image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
1 74836
69.2%
0 31281
28.9%
2 1628
 
1.5%
3 201
 
0.2%
4 57
 
0.1%
5 33
 
< 0.1%
7 30
 
< 0.1%
6 19
 
< 0.1%
9 13
 
< 0.1%
8 12
 
< 0.1%
Other values (23) 62
 
0.1%
ValueCountFrequency (%)
0 31281
28.9%
1 74836
69.2%
2 1628
 
1.5%
3 201
 
0.2%
4 57
 
0.1%
5 33
 
< 0.1%
6 19
 
< 0.1%
7 30
 
< 0.1%
8 12
 
< 0.1%
9 13
 
< 0.1%
ValueCountFrequency (%)
76 1
< 0.1%
66 1
< 0.1%
40 1
< 0.1%
38 1
< 0.1%
35 1
< 0.1%
32 1
< 0.1%
29 2
< 0.1%
26 2
< 0.1%
25 1
< 0.1%
24 1
< 0.1%

PersonsNights
Real number (ℝ)

High correlation  Zeros 

Distinct60
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4120197
Minimum0
Maximum116
Zeros31285
Zeros (%)28.9%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2025-03-03T15:07:49.697776image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4
Q36
95-th percentile12
Maximum116
Range116
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.6629624
Coefficient of variation (CV)1.0568771
Kurtosis12.785162
Mean4.4120197
Median Absolute Deviation (MAD)4
Skewness1.9829592
Sum477257
Variance21.743219
MonotonicityNot monotonic
2025-03-03T15:07:49.824220image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 31285
28.9%
6 16131
14.9%
4 12225
 
11.3%
2 11403
 
10.5%
8 10166
 
9.4%
1 5035
 
4.7%
3 4885
 
4.5%
10 4273
 
4.0%
12 3922
 
3.6%
9 2250
 
2.1%
Other values (50) 6597
 
6.1%
ValueCountFrequency (%)
0 31285
28.9%
1 5035
 
4.7%
2 11403
 
10.5%
3 4885
 
4.5%
4 12225
 
11.3%
5 1093
 
1.0%
6 16131
14.9%
7 291
 
0.3%
8 10166
 
9.4%
9 2250
 
2.1%
ValueCountFrequency (%)
116 1
< 0.1%
99 1
< 0.1%
91 1
< 0.1%
80 1
< 0.1%
75 1
< 0.1%
68 2
< 0.1%
62 1
< 0.1%
60 1
< 0.1%
59 1
< 0.1%
57 1
< 0.1%

RoomNights
Real number (ℝ)

High correlation  Zeros 

Distinct49
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.242022
Minimum0
Maximum185
Zeros31281
Zeros (%)28.9%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2025-03-03T15:07:49.955703image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q33
95-th percentile6
Maximum185
Range185
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.3174968
Coefficient of variation (CV)1.0336637
Kurtosis491.73655
Mean2.242022
Median Absolute Deviation (MAD)2
Skewness9.2629195
Sum242524
Variance5.3707913
MonotonicityNot monotonic
2025-03-03T15:07:50.071328image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
0 31281
28.9%
3 20428
18.9%
2 16545
15.3%
4 13936
12.9%
1 13408
12.4%
5 6200
 
5.7%
7 2567
 
2.4%
6 2419
 
2.2%
8 505
 
0.5%
9 271
 
0.3%
Other values (39) 612
 
0.6%
ValueCountFrequency (%)
0 31281
28.9%
1 13408
12.4%
2 16545
15.3%
3 20428
18.9%
4 13936
12.9%
5 6200
 
5.7%
6 2419
 
2.2%
7 2567
 
2.4%
8 505
 
0.5%
9 271
 
0.3%
ValueCountFrequency (%)
185 1
< 0.1%
116 1
< 0.1%
95 1
< 0.1%
88 2
< 0.1%
59 1
< 0.1%
51 2
< 0.1%
49 1
< 0.1%
48 1
< 0.1%
42 2
< 0.1%
40 2
< 0.1%

DistributionChannel
Categorical

High correlation  Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.6 MiB
Travel Agent/Operator
87710 
Direct
16691 
Corporate
 
3075
GDS Systems
 
696

Length

Max length21
Median length21
Mean length18.280026
Min length6

Characters and Unicode

Total characters1977387
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCorporate
2nd rowTravel Agent/Operator
3rd rowTravel Agent/Operator
4th rowTravel Agent/Operator
5th rowTravel Agent/Operator

Common Values

ValueCountFrequency (%)
Travel Agent/Operator 87710
81.1%
Direct 16691
 
15.4%
Corporate 3075
 
2.8%
GDS Systems 696
 
0.6%

Length

2025-03-03T15:07:50.188848image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-03T15:07:50.345654image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
travel 87710
44.6%
agent/operator 87710
44.6%
direct 16691
 
8.5%
corporate 3075
 
1.6%
gds 696
 
0.4%
systems 696
 
0.4%

Most occurring characters

ValueCountFrequency (%)
r 285971
14.5%
e 283592
14.3%
t 195882
 
9.9%
a 178495
 
9.0%
o 93860
 
4.7%
p 90785
 
4.6%
88406
 
4.5%
T 87710
 
4.4%
/ 87710
 
4.4%
O 87710
 
4.4%
Other values (14) 497266
25.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1977387
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 285971
14.5%
e 283592
14.3%
t 195882
 
9.9%
a 178495
 
9.0%
o 93860
 
4.7%
p 90785
 
4.6%
88406
 
4.5%
T 87710
 
4.4%
/ 87710
 
4.4%
O 87710
 
4.4%
Other values (14) 497266
25.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1977387
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 285971
14.5%
e 283592
14.3%
t 195882
 
9.9%
a 178495
 
9.0%
o 93860
 
4.7%
p 90785
 
4.6%
88406
 
4.5%
T 87710
 
4.4%
/ 87710
 
4.4%
O 87710
 
4.4%
Other values (14) 497266
25.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1977387
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 285971
14.5%
e 283592
14.3%
t 195882
 
9.9%
a 178495
 
9.0%
o 93860
 
4.7%
p 90785
 
4.6%
88406
 
4.5%
T 87710
 
4.4%
/ 87710
 
4.4%
O 87710
 
4.4%
Other values (14) 497266
25.1%

MarketSegment
Categorical

High correlation 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.5 MiB
Other
62572 
Direct
16190 
Travel Agent/Operator
15367 
Groups
10267 
Corporate
 
2844
Other values (2)
 
932

Length

Max length21
Median length5
Mean length7.6783364
Min length5

Characters and Unicode

Total characters830581
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCorporate
2nd rowTravel Agent/Operator
3rd rowTravel Agent/Operator
4th rowTravel Agent/Operator
5th rowTravel Agent/Operator

Common Values

ValueCountFrequency (%)
Other 62572
57.8%
Direct 16190
 
15.0%
Travel Agent/Operator 15367
 
14.2%
Groups 10267
 
9.5%
Corporate 2844
 
2.6%
Complementary 644
 
0.6%
Aviation 288
 
0.3%

Length

2025-03-03T15:07:50.472034image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-03T15:07:50.596593image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
other 62572
50.6%
direct 16190
 
13.1%
travel 15367
 
12.4%
agent/operator 15367
 
12.4%
groups 10267
 
8.3%
corporate 2844
 
2.3%
complementary 644
 
0.5%
aviation 288
 
0.2%

Most occurring characters

ValueCountFrequency (%)
r 141462
17.0%
e 128995
15.5%
t 113272
13.6%
O 77939
9.4%
h 62572
 
7.5%
a 34510
 
4.2%
o 32254
 
3.9%
p 29122
 
3.5%
i 16766
 
2.0%
n 16299
 
2.0%
Other values (15) 177390
21.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 830581
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 141462
17.0%
e 128995
15.5%
t 113272
13.6%
O 77939
9.4%
h 62572
 
7.5%
a 34510
 
4.2%
o 32254
 
3.9%
p 29122
 
3.5%
i 16766
 
2.0%
n 16299
 
2.0%
Other values (15) 177390
21.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 830581
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 141462
17.0%
e 128995
15.5%
t 113272
13.6%
O 77939
9.4%
h 62572
 
7.5%
a 34510
 
4.2%
o 32254
 
3.9%
p 29122
 
3.5%
i 16766
 
2.0%
n 16299
 
2.0%
Other values (15) 177390
21.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 830581
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 141462
17.0%
e 128995
15.5%
t 113272
13.6%
O 77939
9.4%
h 62572
 
7.5%
a 34510
 
4.2%
o 32254
 
3.9%
p 29122
 
3.5%
i 16766
 
2.0%
n 16299
 
2.0%
Other values (15) 177390
21.4%

SRAccessibleRoom
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.8 MiB
0
108147 
1
 
25

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters108172
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 108147
> 99.9%
1 25
 
< 0.1%

Length

2025-03-03T15:07:50.710887image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-03T15:07:50.808198image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
0 108147
> 99.9%
1 25
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 108147
> 99.9%
1 25
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 108172
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 108147
> 99.9%
1 25
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 108172
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 108147
> 99.9%
1 25
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 108172
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 108147
> 99.9%
1 25
 
< 0.1%

SRCrib
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.8 MiB
0
106389 
1
 
1783

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters108172
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 106389
98.4%
1 1783
 
1.6%

Length

2025-03-03T15:07:50.891818image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-03T15:07:51.048800image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
0 106389
98.4%
1 1783
 
1.6%

Most occurring characters

ValueCountFrequency (%)
0 106389
98.4%
1 1783
 
1.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 108172
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 106389
98.4%
1 1783
 
1.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 108172
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 106389
98.4%
1 1783
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 108172
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 106389
98.4%
1 1783
 
1.6%

SRNoAlcoholInMiniBar
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.8 MiB
0
108150 
1
 
22

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters108172
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 108150
> 99.9%
1 22
 
< 0.1%

Length

2025-03-03T15:07:51.144976image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-03T15:07:51.263162image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
0 108150
> 99.9%
1 22
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 108150
> 99.9%
1 22
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 108172
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 108150
> 99.9%
1 22
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 108172
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 108150
> 99.9%
1 22
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 108172
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 108150
> 99.9%
1 22
 
< 0.1%

SRQuietRoom
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.8 MiB
0
98432 
1
 
9740

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters108172
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 98432
91.0%
1 9740
 
9.0%

Length

2025-03-03T15:07:51.378055image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-03T15:07:51.512970image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
0 98432
91.0%
1 9740
 
9.0%

Most occurring characters

ValueCountFrequency (%)
0 98432
91.0%
1 9740
 
9.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 108172
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 98432
91.0%
1 9740
 
9.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 108172
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 98432
91.0%
1 9740
 
9.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 108172
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 98432
91.0%
1 9740
 
9.0%

Floor_asked
Categorical

Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size11.9 MiB
Não espcifico
103238 
HighFloor
 
4703
LowFloor
 
144
MediumFloor
 
84
0
 
3

Length

Max length13
Median length13
Mean length12.81755
Min length1

Characters and Unicode

Total characters1386500
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNão espcifico
2nd rowNão espcifico
3rd rowNão espcifico
4th rowNão espcifico
5th rowNão espcifico

Common Values

ValueCountFrequency (%)
Não espcifico 103238
95.4%
HighFloor 4703
 
4.3%
LowFloor 144
 
0.1%
MediumFloor 84
 
0.1%
0 3
 
< 0.1%

Length

2025-03-03T15:07:51.623033image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-03T15:07:51.725632image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
não 103238
48.8%
espcifico 103238
48.8%
highfloor 4703
 
2.2%
lowfloor 144
 
0.1%
mediumfloor 84
 
< 0.1%
0 3
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
o 216482
15.6%
i 211263
15.2%
c 206476
14.9%
e 103322
7.5%
N 103238
7.4%
103238
7.4%
s 103238
7.4%
p 103238
7.4%
f 103238
7.4%
ã 103238
7.4%
Other values (13) 29529
 
2.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1386500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 216482
15.6%
i 211263
15.2%
c 206476
14.9%
e 103322
7.5%
N 103238
7.4%
103238
7.4%
s 103238
7.4%
p 103238
7.4%
f 103238
7.4%
ã 103238
7.4%
Other values (13) 29529
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1386500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 216482
15.6%
i 211263
15.2%
c 206476
14.9%
e 103322
7.5%
N 103238
7.4%
103238
7.4%
s 103238
7.4%
p 103238
7.4%
f 103238
7.4%
ã 103238
7.4%
Other values (13) 29529
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1386500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 216482
15.6%
i 211263
15.2%
c 206476
14.9%
e 103322
7.5%
N 103238
7.4%
103238
7.4%
s 103238
7.4%
p 103238
7.4%
f 103238
7.4%
ã 103238
7.4%
Other values (13) 29529
 
2.1%

Bath asked
Categorical

Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size12.0 MiB
Não espcifico
107640 
Bathtub
 
350
Showe
 
182

Length

Max length13
Median length13
Mean length12.967126
Min length5

Characters and Unicode

Total characters1402680
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNão espcifico
2nd rowNão espcifico
3rd rowNão espcifico
4th rowNão espcifico
5th rowNão espcifico

Common Values

ValueCountFrequency (%)
Não espcifico 107640
99.5%
Bathtub 350
 
0.3%
Showe 182
 
0.2%

Length

2025-03-03T15:07:51.878085image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-03T15:07:52.071192image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
não 107640
49.9%
espcifico 107640
49.9%
bathtub 350
 
0.2%
showe 182
 
0.1%

Most occurring characters

ValueCountFrequency (%)
o 215462
15.4%
c 215280
15.3%
i 215280
15.3%
e 107822
7.7%
N 107640
7.7%
ã 107640
7.7%
f 107640
7.7%
p 107640
7.7%
s 107640
7.7%
107640
7.7%
Other values (8) 2996
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1402680
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 215462
15.4%
c 215280
15.3%
i 215280
15.3%
e 107822
7.7%
N 107640
7.7%
ã 107640
7.7%
f 107640
7.7%
p 107640
7.7%
s 107640
7.7%
107640
7.7%
Other values (8) 2996
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1402680
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 215462
15.4%
c 215280
15.3%
i 215280
15.3%
e 107822
7.7%
N 107640
7.7%
ã 107640
7.7%
f 107640
7.7%
p 107640
7.7%
s 107640
7.7%
107640
7.7%
Other values (8) 2996
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1402680
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 215462
15.4%
c 215280
15.3%
i 215280
15.3%
e 107822
7.7%
N 107640
7.7%
ã 107640
7.7%
f 107640
7.7%
p 107640
7.7%
s 107640
7.7%
107640
7.7%
Other values (8) 2996
 
0.2%

Bed_asked
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size9.8 MiB
Não espcifico
52302 
KingSizeBed
39679 
TwinBed
15922 
0
 
269

Length

Max length13
Median length11
Mean length11.353382
Min length1

Characters and Unicode

Total characters1228118
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNão espcifico
2nd rowNão espcifico
3rd rowNão espcifico
4th rowNão espcifico
5th rowNão espcifico

Common Values

ValueCountFrequency (%)
Não espcifico 52302
48.4%
KingSizeBed 39679
36.7%
TwinBed 15922
 
14.7%
0 269
 
0.2%

Length

2025-03-03T15:07:52.180697image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-03T15:07:52.335006image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
não 52302
32.6%
espcifico 52302
32.6%
kingsizebed 39679
24.7%
twinbed 15922
 
9.9%
0 269
 
0.2%

Most occurring characters

ValueCountFrequency (%)
i 199884
16.3%
e 147582
12.0%
o 104604
 
8.5%
c 104604
 
8.5%
n 55601
 
4.5%
d 55601
 
4.5%
B 55601
 
4.5%
N 52302
 
4.3%
52302
 
4.3%
s 52302
 
4.3%
Other values (10) 347735
28.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1228118
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 199884
16.3%
e 147582
12.0%
o 104604
 
8.5%
c 104604
 
8.5%
n 55601
 
4.5%
d 55601
 
4.5%
B 55601
 
4.5%
N 52302
 
4.3%
52302
 
4.3%
s 52302
 
4.3%
Other values (10) 347735
28.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1228118
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 199884
16.3%
e 147582
12.0%
o 104604
 
8.5%
c 104604
 
8.5%
n 55601
 
4.5%
d 55601
 
4.5%
B 55601
 
4.5%
N 52302
 
4.3%
52302
 
4.3%
s 52302
 
4.3%
Other values (10) 347735
28.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1228118
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 199884
16.3%
e 147582
12.0%
o 104604
 
8.5%
c 104604
 
8.5%
n 55601
 
4.5%
d 55601
 
4.5%
B 55601
 
4.5%
N 52302
 
4.3%
52302
 
4.3%
s 52302
 
4.3%
Other values (10) 347735
28.3%

Distance_elevator_asked
Categorical

Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.8 MiB
No specific
107734 
Away from elevator
 
401
Near Elevator
 
37

Length

Max length18
Median length11
Mean length11.026634
Min length11

Characters and Unicode

Total characters1192773
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo specific
2nd rowNo specific
3rd rowNo specific
4th rowNo specific
5th rowNo specific

Common Values

ValueCountFrequency (%)
No specific 107734
99.6%
Away from elevator 401
 
0.4%
Near Elevator 37
 
< 0.1%

Length

2025-03-03T15:07:52.445865image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-03T15:07:52.561790image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
ValueCountFrequency (%)
no 107734
49.7%
specific 107734
49.7%
elevator 438
 
0.2%
away 401
 
0.2%
from 401
 
0.2%
near 37
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
c 215468
18.1%
i 215468
18.1%
e 108610
9.1%
108573
9.1%
o 108573
9.1%
f 108135
9.1%
N 107771
9.0%
s 107734
9.0%
p 107734
9.0%
a 876
 
0.1%
Other values (9) 3831
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1192773
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
c 215468
18.1%
i 215468
18.1%
e 108610
9.1%
108573
9.1%
o 108573
9.1%
f 108135
9.1%
N 107771
9.0%
s 107734
9.0%
p 107734
9.0%
a 876
 
0.1%
Other values (9) 3831
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1192773
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
c 215468
18.1%
i 215468
18.1%
e 108610
9.1%
108573
9.1%
o 108573
9.1%
f 108135
9.1%
N 107771
9.0%
s 107734
9.0%
p 107734
9.0%
a 876
 
0.1%
Other values (9) 3831
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1192773
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
c 215468
18.1%
i 215468
18.1%
e 108610
9.1%
108573
9.1%
o 108573
9.1%
f 108135
9.1%
N 107771
9.0%
s 107734
9.0%
p 107734
9.0%
a 876
 
0.1%
Other values (9) 3831
 
0.3%

Interactions

2025-03-03T15:07:43.705516image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-03T15:07:36.097181image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-03T15:07:37.030564image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-03T15:07:38.209005image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-03T15:07:39.147054image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-03T15:07:39.983224image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-03T15:07:40.906480image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-03T15:07:42.133812image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-03T15:07:43.958436image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-03T15:07:36.233431image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-03T15:07:37.159956image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-03T15:07:38.316675image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-03T15:07:39.248792image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-03T15:07:40.085131image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-03T15:07:41.027148image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-03T15:07:42.305905image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-03T15:07:44.188115image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-03T15:07:36.345829image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-03T15:07:37.286198image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-03T15:07:38.427355image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-03T15:07:39.353215image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-03T15:07:40.214032image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-03T15:07:41.147801image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-03T15:07:42.520544image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-03T15:07:44.451187image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-03T15:07:36.460979image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-03T15:07:37.401469image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-03T15:07:38.534739image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-03T15:07:39.457351image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-03T15:07:40.333050image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-03T15:07:41.260262image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-03T15:07:42.701127image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-03T15:07:44.597213image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-03T15:07:36.574389image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-03T15:07:37.684516image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-03T15:07:38.674190image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-03T15:07:39.550950image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-03T15:07:40.444515image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-03T15:07:41.367760image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-03T15:07:42.867688image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-03T15:07:44.763027image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-03T15:07:36.688369image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-03T15:07:37.795763image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-03T15:07:38.796861image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-03T15:07:39.656903image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-03T15:07:40.556302image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-03T15:07:41.479983image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-03T15:07:43.049333image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-03T15:07:45.001853image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-03T15:07:36.801381image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-03T15:07:37.909911image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-03T15:07:38.931661image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-03T15:07:39.771465image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-03T15:07:40.668349image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-03T15:07:41.657068image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-03T15:07:43.265163image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-03T15:07:45.176589image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-03T15:07:36.908483image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-03T15:07:38.041612image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-03T15:07:39.038037image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-03T15:07:39.878306image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-03T15:07:40.783971image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-03T15:07:41.877656image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2025-03-03T15:07:43.476578image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Correlations

2025-03-03T15:07:52.658225image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
AgeAverageLeadTimeBath askedBed_askedBookingsCheckedInDaysSinceCreationDistance_elevator_askedDistributionChannelFloor_askedLodgingRevenueMarketSegmentOtherRevenuePersonsNightsRoomNightsSRAccessibleRoomSRCribSRNoAlcoholInMiniBarSRQuietRoom
Age1.0000.2300.0260.0750.1860.1250.0200.0720.0170.1250.1190.2120.1530.1600.0130.1940.0120.053
AverageLeadTime0.2301.0000.0140.0550.7360.2740.0000.0790.0090.6820.1060.7160.7350.7200.0000.0430.0000.032
Bath asked0.0260.0141.0000.0230.0000.0210.0170.0270.0270.0000.0310.0000.0090.0010.0290.0280.0000.017
Bed_asked0.0750.0550.0231.0000.0240.0840.0100.1260.0540.0080.2200.0000.0320.0230.0110.0510.0110.112
BookingsCheckedIn0.1860.7360.0000.0241.0000.3800.0010.0690.0000.7840.0550.7830.7870.7980.0000.0000.0000.000
DaysSinceCreation0.1250.2740.0210.0840.3801.0000.0180.0620.0300.2310.0680.3040.3100.2940.0110.0560.0150.132
Distance_elevator_asked0.0200.0000.0170.0100.0010.0181.0000.0170.0990.0000.0230.0000.0020.0000.0980.0050.0000.066
DistributionChannel0.0720.0790.0270.1260.0690.0620.0171.0000.0230.0190.7210.0170.0330.0580.0000.0480.0000.093
Floor_asked0.0170.0090.0270.0540.0000.0300.0990.0231.0000.0000.0540.0000.0000.0000.0150.0000.0000.076
LodgingRevenue0.1250.6820.0000.0080.7840.2310.0000.0190.0001.0000.0180.8350.8890.9050.0000.0050.0000.008
MarketSegment0.1190.1060.0310.2200.0550.0680.0230.7210.0540.0181.0000.0160.0440.0510.0000.0580.0050.212
OtherRevenue0.2120.7160.0000.0000.7830.3040.0000.0170.0000.8350.0161.0000.8670.8430.0000.0020.0000.000
PersonsNights0.1530.7350.0090.0320.7870.3100.0020.0330.0000.8890.0440.8671.0000.9510.0000.0120.0000.019
RoomNights0.1600.7200.0010.0230.7980.2940.0000.0580.0000.9050.0510.8430.9511.0000.0000.0000.0000.000
SRAccessibleRoom0.0130.0000.0290.0110.0000.0110.0980.0000.0150.0000.0000.0000.0000.0001.0000.0000.0000.000
SRCrib0.1940.0430.0280.0510.0000.0560.0050.0480.0000.0050.0580.0020.0120.0000.0001.0000.0100.006
SRNoAlcoholInMiniBar0.0120.0000.0000.0110.0000.0150.0000.0000.0000.0000.0050.0000.0000.0000.0000.0101.0000.012
SRQuietRoom0.0530.0320.0170.1120.0000.1320.0660.0930.0760.0080.2120.0000.0190.0000.0000.0060.0121.000

Missing values

2025-03-03T15:07:45.487853image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-03-03T15:07:46.040009image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

NationalityAgeDaysSinceCreationAverageLeadTimeLodgingRevenueOtherRevenueBookingsCheckedInPersonsNightsRoomNightsDistributionChannelMarketSegmentSRAccessibleRoomSRCribSRNoAlcoholInMiniBarSRQuietRoomFloor_askedBath askedBed_askedDistance_elevator_asked
ID
1PRT52.044059292.082.3264CorporateCorporate0000Não espcificoNão espcificoNão espcificoNo specific
2PRT53.6138561280.053.01105Travel Agent/OperatorTravel Agent/Operator0000Não espcificoNão espcificoNão espcificoNo specific
3DEU32.0138500.00.0000Travel Agent/OperatorTravel Agent/Operator0000Não espcificoNão espcificoNão espcificoNo specific
4FRA61.0138593240.060.01105Travel Agent/OperatorTravel Agent/Operator0000Não espcificoNão espcificoNão espcificoNo specific
5FRA52.0138500.00.0000Travel Agent/OperatorTravel Agent/Operator0000Não espcificoNão espcificoNão espcificoNo specific
6JPN55.0138558230.024.0142Travel Agent/OperatorOther0000Não espcificoNão espcificoNão espcificoNo specific
7JPN50.0138500.00.0000Travel Agent/OperatorOther0000Não espcificoNão espcificoNão espcificoNo specific
8FRA33.0138538535.094.01105Travel Agent/OperatorOther0000Não espcificoNão espcificoKingSizeBedNo specific
9FRA43.0138500.00.0000Travel Agent/OperatorOther0000Não espcificoNão espcificoKingSizeBedNo specific
10IRL26.0138596174.069.0163Travel Agent/OperatorTravel Agent/Operator0000Não espcificoNão espcificoNão espcificoNo specific
NationalityAgeDaysSinceCreationAverageLeadTimeLodgingRevenueOtherRevenueBookingsCheckedInPersonsNightsRoomNightsDistributionChannelMarketSegmentSRAccessibleRoomSRCribSRNoAlcoholInMiniBarSRQuietRoomFloor_askedBath askedBed_askedDistance_elevator_asked
ID
111724ITA56.03700.000.0000Travel Agent/OperatorOther0000HighFloorNão espcificoKingSizeBedNo specific
111725ESP60.03743875.00167.81105Travel Agent/OperatorOther0000HighFloorNão espcificoTwinBedNear Elevator
111726PAN60.03700.000.0000Travel Agent/OperatorOther0000HighFloorNão espcificoTwinBedNear Elevator
111727PRT51.0377173.5518.0111DirectDirect0001HighFloorNão espcificoNão espcificoNo specific
111728DEU34.0364198.0014.0121Travel Agent/OperatorTravel Agent/Operator0000Não espcificoNão espcificoKingSizeBedNo specific
111729DEU31.03600.000.0000Travel Agent/OperatorTravel Agent/Operator0000Não espcificoNão espcificoKingSizeBedNo specific
111730BRA43.036170755.2520.01105Travel Agent/OperatorOther0000Não espcificoNão espcificoKingSizeBedNo specific
111731BRA37.03600.000.0000Travel Agent/OperatorOther0000Não espcificoNão espcificoKingSizeBedNo specific
111732DEU48.03666708.00185.0184Travel Agent/OperatorOther0000Não espcificoNão espcificoNão espcificoNo specific
111733DEU48.03600.000.0000Travel Agent/OperatorOther0000Não espcificoNão espcificoNão espcificoNo specific